Image capture and analysis now play a significant role in many industrial applications. For example, they may be employed in determining the orientation of a vehicle or platform, in vehicle navigation, and also in consumer products such as digital cameras and cellular phones. There is a trend toward increased resolution of the images captured, and storing a high-resolution image that includes millions of pixels typically requires a large amount of memory. But, some systems such as cameras in cell phones need to be small in size and, hence, permit the use of only a small on-board memory. Some other systems, such as those aboard satellites and spacecrafts, may be exposed to high radiation, requiring their on-board memory and/or processors to withstand high amounts of radiation. Such memory and processor components are relatively expensive compared to ordinary components of similar size and capacity that cannot withstand high radiation. It is, therefore, desirable to minimize the size of the memory and/or processor used in various imaging devices.
Typically, in image sensing, an image to be captured is divided into a matrix of pixels. The larger the required resolution, the smaller the size of each pixel and, correspondingly, the larger the number of pixels that must be sensed. The term “pixel” typically means a fraction of an image, but it is also commonly used to refer to the circuitry that senses the light received from a corresponding fraction of the image. Both meanings of the term are used herein.
Sensing a pixel generally includes measuring the intensity of the light corresponding to the pixel. In conventional imaging, each sensed pixel is digitized, i.e., the measured intensity of the light, generally expressed in the form of a voltage or current signal, is converted into a number that is stored in a memory cell. As such, high-resolution imaging, which requires a large number of pixels, typically requires a large memory.
Compressive sampling (which is also known as compressive sensing) is one approach that has been employed to meet the conflicting goals of high-resolution imaging and small-size memory. In certain cases, compressive sampling has decreased the required memory significantly, on a logarithmic scale. More specifically, in various existing approaches to compressive sampling, the total number of pixels P in an image are divided into N sets of M pixels, where M is typically much smaller than P. For example, M may be 10 or 100, while P can be as high as hundreds of millions. Then, for each set of M pixels, the values of some pixels are added to obtain a partial sum and the values of some pixels are subtracted from the partial sum to obtain a final pixel value corresponding to the set of M pixels. The pixels that are to be added or subtracted are determined according to a sampling function. For each set of M pixels, only the final pixel value is stored. As a result, compressive sampling generally only requires N memory cells, which is on the order of log P.
Although the final memory-storage requirement of compressive sampling can thus be substantially less than that of conventional imaging, compressive sampling still presents some challenges. As described above, the sampling functions require that a set of sensed pixels be added and/or subtracted. Unfortunately, the hardware used by many existing systems for compressive sampling requires that each pixel in the set of sensed pixels first be individually digitized so that the digitized values may then be added and/or subtracted by a processor. This requires a large temporary memory storage, significant processing capacity, and/or numerous interconnects for transferring the sensed pixels to a processor where the pixels may be digitized and processed (i.e., added or subtracted). In other words, the computational requirements of compressive sampling systems implemented significantly in the digital domain substantially undermine their benefits. Therefore, there is a need for improved systems and methods of sensing pixels that efficiently enable compressive sampling.